class_docstr: Implementation of methods in Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks.
  Wang et al. (2019).
  
  | Paper link: https://people.cs.uchicago.edu/~ravenben/publications/pdf/backdoor-sp19.pdf
class_docstr_tok: ["Implementation","of","methods","in","Neural","Cleanse",":","Identifying","and","Mitigating","Backdoor","Attacks","in","Neural","Networks",".","Wang","et","al",".","(","2019",")",".","|","Paper","link",":","https",":","\/\/people.cs.uchicago.edu\/~ravenben\/publications\/pdf\/backdoor-sp19.pdf"]
docstr: Return the output of the second to last layer for input `x`.
  
  :param x: Input for computing the activations.
  :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
docstr_tok: ["Return","the","output","of","the","second","to","last","layer","for","input","`","x","`",".",":","param","x",":","Input","for","computing","the","activations",".",":","return",":","The","output","of","`","layer","`",",","where","the","first","dimension","is","the","batch","size","corresponding","to","`","x","`","."]
code: def _get_penultimate_layer_activations(
      self, x: np.ndarray
  ) -> np.ndarray:
      """
      Return the output of the second to last layer for input `x`.
  
      :param x: Input for computing the activations.
      :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
      """
      raise NotImplementedError
  
file_path: adversarial-robustness-toolbox/art/estimators/poison_mitigation/neural_cleanse/neural_cleanse.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Union, Tuple, List
  import numpy
  from art.estimators.certification.abstain import AbstainPredictorMixin
  from art.utils import to_categorical
label_desc: Use image node_id 4 for calling the NeuralCleanseMixin obj's underlying member method code with example usage: obj._get_penultimate_layer_activations(x) without return types